Search Results (309)

Against the backdrop of climate change, forest fires increasingly undermine ecosystem stability and reshape species distributions in Heilongjiang Province. Therefore, quantifying the drivers of fire occurrence and conducting long-term fire risk forecasting holds critical value for regional ecological security. Centered on the forested regions of Heilongjiang Province, this study systematically assessed the relative contributions of multi-source factors—including topography, vegetation, and meteorological conditions—to fire occurrence and compared the predictive performance of three models: Deep Neural Network with Residual Connections (ResDNN), Artificial Neural Network (ANN), and Support Vector Machine (SVM). Modeling results based on historical fire records indicated that the ResDNN model achieved the highest accuracy (85.6%). Owing to its robust nonlinear mapping capability, it performed better in capturing complex feature interactions than ANN and SVM. These results demonstrate its strong applicability to forest fire prediction in Heilongjiang Province. Building on these findings, the study employed the best-performing ResDNN model in conjunction with CMIP6 multi-model climate projections to simulate and map the spatiotemporal probability of forest fire occurrence from 2030 to 2070. The results provide an intuitive representation of long-term fire-risk trajectories under future climate scenarios and offer scientific support for regional fire prevention, monitoring, early-warning systems, and forest management under climate change. Full article

Tropical cyclones kill thousands and inflict vast destruction annually. While ocean temperatures and atmospheric conditions dominate their formation and behaviour, forests’ potential influence has received little systematic attention. This review examines whether and how forests may affect tropical cyclone frequency, intensity, and behaviour. Support varies by mechanism and stage. Post-landfall effects have the strongest support: forests slow storms, moderate wind speeds and curb flooding through enhanced soil infiltration. Forests also influence storm tracks, though magnitudes are uncertain. Pre-landfall effects are less certain. These include processes that modify offshore humidity, temperature, and aerosols. The Biotic Pump theory proposes that forest cover creates pressure gradients drawing moisture inland, reducing its availability for ocean storms. Forest influences are likely to be most evident near thresholds for storm formation or intensification, where small perturbations in conditions can alter outcomes. This context-dependency reconciles divergent findings and aids the integration of forests into climate risk assessments. Forest conservation provides clear post-landfall protection; pre-landfall effects, while uncertain, further strengthen the case for protection and highlight research priorities. Full article

Robinia pseudoacacia is a major tree species for soil and water conservation afforestation in the “Three-North” Region, with crucial ecological improvement and carbon sequestration functions. This study aimed to investigate the dynamics of suitable areas and carbon storage of R. pseudoacacia plantations under different future climate scenarios, further reveal the changing trend of their carbon sequestration potential, and provide a scientific basis for the rational layout and sustainable management of R. pseudoacacia plantations in the “Ω”-shaped bend of the Yellow River. Based on the MaxEnt model, we predicted the potential suitable distribution of R. pseudoacacia under future climate change scenarios, identified the potentially threatened geographical distribution regions and area changes in R. pseudoacacia, and clarified the limiting factors affecting the potential geographical distribution of R. pseudoacacia plantations by analyzing the contribution rates and permutation importance of comprehensive environmental variables. Combined with the InVEST model, we estimated and analyzed the spatial distribution of carbon storage in R. pseudoacacia plantations in the 2090s. The results showed that the minimum temperature of the coldest month was the main environmental factor affecting the distribution of potential suitable areas of R. pseudoacacia plantations, with a contribution rate of 46.98%, followed by annual precipitation. Under current climatic conditions, the potential suitable areas of R. pseudoacacia plantations were mainly distributed in the Loess Plateau, Hetao Plain, Ordos Plateau, Kubuqi Desert, and northern Mu Us Sandy Land. The highly suitable areas were mainly concentrated in the south-central part of the Loess Plateau, accounting for approximately 22.81% of the total area of the “Ω”-shaped bend of the Yellow River. Under future climate change, the moderately and highly suitable areas tended to shift northwestward. Under the four future climate scenarios, the carbon storage and carbon density of R. pseudoacacia plantations showed a trend of first increasing and then decreasing; by 2100, the carbon storage reached the maximum under the SSP370 scenario, and the areas with medium-to-high carbon storage first expanded and then contracted, mainly concentrated in the Ordos Plateau and Loess Plateau. Full article

Addressing the challenges in predicting carbon–water fluxes in cold, temperate coniferous forests—specifically, the strong heterogeneity of driving factors, the significant non-linearity of processes, and the lack of consistency of ecological mechanisms in data-driven models—this paper constructs a Multi-Channel Fusion Attention BiLSTM (MCF-ABiLSTM) model. This model is designed for the joint prediction of Net Ecosystem Exchange (NEE) and Latent Heat Flux (LE). The model adopts a multi-channel structure to separately characterize meteorological, soil, and historical flux information, combining channel attention and temporal attention mechanisms to enhance the identification of key driving factors and critical temporal scales. On this basis, dynamic Water Use Efficiency (dWUE) and Sensitivity of Carbon–Water (SCW) indices are proposed to characterize the synergistic response features of carbon uptake and evapotranspiration under humidity and temperature gradients. The stable ecological relationships revealed by these indices are explicitly introduced into the model training process as ecological process consistency constraints, thereby guiding the model to adhere to known physiological mechanisms while improving prediction accuracy. Experimental results demonstrate that the MCF-ABiLSTM model outperforms various benchmark models in predicting both NEE and LE. Furthermore, flux contribution decomposition results indicate that the model’s response structure to environmental drivers is highly consistent with the known carbon–water coupling mechanisms of cold, temperate coniferous forests. This study achieves organic integration of high-precision carbon–water flux prediction, ecological process constraints, and mechanism analysis, providing a modeling framework that possesses both predictive capability and ecological interpretability for research on the carbon–water cycle in cold, temperate forest ecosystems. Full article

Projections of species distribution shifts induced by climate change are essential for adaptive management, yet regional-scale projections that explicitly address uncertainty remain underexplored. Future habitat suitability for Larix principis-rupprechtii in the Haihe Basin is projected using ensemble MaxEnt analysis driven by 13 CMIP6 climate simulations under contrasting emission scenarios (SSP1-2.6 and SSP5-8.5). The MaxEnt demonstrates strong performance, with a mean AUC of 0.874. Future scenarios show that climatically favorable habitat for larch expands by over 20% and shifts approximately 42 km southwestward relative to the baseline, while high-suitability areas increase by 109%–181%. However, substantial uncertainty, quantified by the coefficient of variation (C_V), persists in the low-suitability areas and intensifies with longer time horizons and higher emission pathways. Crucially, local topographic heterogeneity (elevation, slope, and shallow soil moisture) explains over 84% of the distribution variance, overriding broad-scale climatic drivers. We conclude that adaptive revegetation strategies at the regional basin scale should prioritize topographic controls, while the uncertainty in habitat suitability induced by climate change must not be overlooked. Full article

Nighttime temperatures in the Afro-alpine zone (>2050 m) of Kahuzi-Biega National Park frequently fall below 5 °C. However, the thermal advantages provided by night nests of Grauer’s gorilla, Gorilla beringei graueri along this elevation gradient have yet to be quantified. From 3 January to 7 January 2025, 80 night nests were located along the Mt. Kahuzi–Biega ridge (2000–2650 m above sea level); 66 with complete data were analyzed. Nest-interior and ambient temperatures were measured using calibrated mercury thermometers, while canopy openness was assessed through sky-facing photographs analyzed with ImageJ. Canopy openness ranged from 18% at 2050 m (dense bamboo) to 83% at 2625 m (open ericaceous scrub), with a mean of 50.5 ± 18.8%. The interiors of the nests consistently exhibited warmer temperatures than the humid ambient air, with a mean temperature difference of 2.03 ± 0.37 °C, ranging from 1.39 to 2.68 °C. Linear mixed-model analysis (n = 66) indicated a significant reduction in thermal buffering correlated with increasing elevation (β = −7.4 × 10⁻⁴ °C m⁻¹, 95% CI −8.9 × 10⁻⁴ to −5.9 × 10⁻⁴, p < 0.001) and greater canopy openness (β = −0.020 °C per %, p < 0.001); fog density and precipitation from the previous night did not exhibit a significant effect. The model explained 78% of the variance in ΔT (marginal R²). Over a 650 m Afro-alpine gradient, Grauer’s gorillas create a 2.0 °C thermal refuge, which decreases by approximately 30% near the summit. This study represents the first quantitative evidence that canopy density can mitigate the elevation penalty for any African great ape. Canopy retention is the only terrestrial mechanism that can mitigate accelerated warming at high altitudes, which is occurring at a rate of +0.45 °C per decade. Without canopy retention, national conservation strategies for the Democratic Republic of Congo must allocate funds for extended energy subsidies at elevations exceeding 2500 m. Full article

The dried leaves of Turpinia arguta (Lindl.) Seem, a traditional Chinese medicinal herb, have been used for the treatment of tonsillitis, sore throat, throat arthralgia, and novel coronavirus pneumonia. This plant possesses significant medicinal, economic, and ecological values. Assessing its distribution patterns and its response to global climate change is critical for the conservation and sustainable use of its resources. This study used GIS technology and ENMTools v1.3 to select 247 distribution records of T. arguta and employed the kuenm R package (running on R v4.4.3, package version 2.0.1) to optimize the MaxEnt model parameters. Based on current and future climate data, this study predicted the current and future potential suitable areas of T. arguta in China during the periods of the 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100) under three SSP emission scenarios (SSP126, SSP245, and SSP585). Additionally, it identified the key environmental variables driving its distribution patterns and conducted a quality suitability regionalization analysis using sample chemical content data. The results show that under current climatic conditions, the highly suitable areas for T. arguta are mainly distributed across five provinces: Jiangxi, Guangdong, Guangxi, Fujian, and Hunan. The distribution of T. arguta is primarily influenced by precipitation and temperature. The suitable ranges of key environmental variables are as follows: average temperature in September > 26 °C (optimal range: 28–32 °C), precipitation in April 175–250 mm, precipitation in September 100–160 mm, annual mean temperature 20–30 °C (optimal range > 22.5 °C), and annual precipitation 1500–2000 mm (peak value: 1750 mm). Quality analysis reveals a positive correlation between ligustroflavone content and the mean diurnal temperature range, as well as between rhoifolin content and soil sand content. Compared with current suitable areas, the total suitable areas of T. arguta are projected to contract by varying degrees across all scenarios in the future. This study will provide a robust scientific basis for guiding the sustainable development/utilization of its resources and optimizing artificial cultivation practices. Full article

Rainfall is an essential component of boreal forest ecosystems. Aerotechnogenic pollution significantly affects the composition of rainfall. To predict the dynamics of biogeochemical cycles and develop strategies to enhance forest resilience in the Arctic zone, it is necessary to study the composition and characteristics of rainfall. The objective of this study is to evaluate the variation in the chemical composition of stemflow in the most typical pine and spruce forests of Fennoscandia under conditions of aerotechnogenic pollution based on long-term monitoring data from 1999 to 2022. The research was carried out in forests exposed to atmospheric industrial pollution from the largest copper–nickel smelter in northern Europe (Murmansk Region, Russia). The study of rainwater composition was conducted in four microsites: open areas (OA), between crowns (BWC), below crowns (BC) and stemflow (SF). A significant influence of the tree canopy on the rainfall composition was noted. Stemflow was found to have the highest concentration of pollutants, indicating a significant biochemical role of this type of precipitation. The results showed an increase in the concentrations of heavy metals and sulfates in rainwater as we moved closer to the pollution source. Below crowns and in the stemflow of spruce forests, element concentrations are higher compared to pine forests. The highest concentrations of major pollutants in stemflow (Ni, Cu and SO₄²⁻) are observed in June—at the beginning of the growing season. Long-term dynamics reveal a decrease in the concentrations of Cu, Cd and Cr in defoliated forests and technogenic sparse forests. Stemflow volume rises from background to technogenic sparse forests due to deteriorating tree-crown conditions. This is associated with the deteriorating condition of tree stands, as manifested by reductions in tree height, diameter and needle cover. It has been established that under pollution conditions, trees’ assimilating organs actively accumulate heavy metals, thereby altering the composition of precipitation passing through the canopy. Full article

Understanding how climatic variability affects growth and water relations of Scots pine (Pinus sylvestris L.) is essential for assessing stand sustainability in hemi-boreal regions. Linear mixed-effects models were used to quantify the effects of climatic variability and tree characteristics on stem volume increment (ZV), sap flow (SF), and water-use efficiency (WUE) of Scots pine growing on highly oligotrophic soils in Curonian Spit National Park. Annual ZV was strongly controlled by tree size and seasonal temperature conditions. Higher temperatures in late winter and mid-summer enhanced growth, whereas elevated temperatures in April–May reduced increment. June moisture availability, expressed by the hydrothermal coefficient, had a positive effect, highlighting the sensitivity of growth to early-summer drought and heat waves. Sap-flow density during May–October was primarily driven by climatic factors, with temperature stimulating and relative humidity reducing SF, while tree size played a minor role. Random-effects analysis showed that unexplained variability in ZV was mainly associated with persistent differences among trees and sites, whereas SF variability occurred largely at the within-tree level. In contrast, WUE was dominated by climatic drivers, with no detectable site- or tree-level random effects. Higher June precipitation increased WUE, while warmer growing-season conditions reduced it. Overall, Scots pine growth and WUE are mainly regulated by intra-annual climatic conditions, particularly summer water availability. Despite rapid climatic change, no critical physiological thresholds or growth collapse were detected during the study period, indicating substantial adaptive capacity of Scots pine even under the observed exceptional conditions. Full article

Air pollution is a major but often under-integrated driver of forest dynamics at the global scale. This review combines a bibliometric analysis of 258 peer-reviewed studies with a synthesis of ecological, physiological, and biogeochemical evidence to clarify how multiple air pollutants influence forest structure, function, and regeneration. Research output is dominated by Europe, East Asia, and North America, with ozone, nitrogen deposition, particulate matter, and acidic precipitation receiving the greatest attention. Across forest biomes, air pollution affects growth, wood anatomy, nutrient cycling, photosynthesis, species composition, litter decomposition, and soil chemistry through interacting pathways. Regional patterns reveal strong context dependency, with heightened sensitivity in mountain and boreal forests, pronounced ozone exposure in Mediterranean and peri-urban systems, episodic oxidative stress in tropical forests, and long-term heavy-metal accumulation in industrial regions. Beyond being impacted, forests actively modify atmospheric chemistry through pollutant filtration, aerosol interactions, and deposition processes. The novelty of this review lies in explicitly framing air pollution as a dynamic driver of forest change, with direct implications for afforestation and restoration on degraded lands. Key knowledge gaps remain regarding combined pollution–climate effects, understudied forest biomes, and the scaling of physiological responses to ecosystem and regional levels, which must be addressed to support effective forest management under global change. Full article

Forests play a central role in climate change mitigation by acting as biogenic carbon reservoirs and providing renewable biomass for energy systems. In Portugal, where fire-prone landscapes and species composition dynamics pose increasing management challenges, understanding the carbon storage potential of forest biomass is crucial for designing effective decarbonization strategies. This study provides a comprehensive characterization of the Portuguese forest and quantifies the biogenic carbon stored in live and dead biomass across the main forest species. Species-specific carbon contents, rather than the conventional 50% assumption widely used in the literature, were applied to National Forest Inventory data, enabling more realistic and representative carbon stock estimates expressed in kilotonnes of CO₂ equivalent. While the approach relies on inventory-based biomass data and literature-derived carbon fractions and is therefore subject to associated uncertainties, it provides an improved representation of species-level carbon storage at the national scale. Results show that Pinus pinaster, Eucalyptus globulus, and Quercus suber together represent the largest share of carbon storage, with approximately 300,000 kilotonnes of CO₂ equivalent retained in living trees. Wood is the dominant carbon pool, but roots and branches also account for a substantial fraction, emphasizing the need to consider both above- and below-ground biomass in carbon accounting. In parallel, a bibliometric analysis based on the systematic evaluation of scientific publications was conducted to characterize the evolution, thematic focus, and geographic distribution of global research on forest-based biogenic carbon. This analysis reveals a rapidly expanding scientific interest in biogenic carbon, particularly since 2020, reflecting its growing relevance in climate change mitigation frameworks. Overall, the results underscore both the strategic importance of Portuguese forests and the alignment of this research with the broader international scientific agenda on forest-based biogenic carbon. Full article

Pinus ayacahuite is an important species for reforestation in Mexico, as it is a pioneer species in open areas. Its regeneration could be threatened by rising temperatures. The effect of a temperature gradient on germination was analyzed, and potential distribution projections of climate change scenarios were modeled at various time scales. Seeds were collected in Huayacocotla, Veracruz; these were germinated under nine constant temperatures (5–45 °C). Germination parameters, cardinal temperatures, and thermal time were estimated using a Gaussian model. Germination occurred between 10 and 40 °C, with optimal, base, and ceiling temperatures of 27 °C, 10 °C, and 42 °C, respectively, and a thermal time (Tt₅₀) of 118.5 °C d⁻¹. Based on climate change projections (SSP1-2.6 and SSP5-8.5), NASA’s GISS-E2-1-G model predicts temperature increases from 1.1 to 2.3 °C by 2050 and from 1.7 to 3.6 °C by 2090, which would accelerate germination by 12.9–25 days. However, the species’ potential distribution is projected to decline by 15%–22%, primarily in southern states such as Chiapas, Oaxaca, and Puebla, although it could shift to new suitable areas in Tamaulipas and Nuevo León. These results suggest that while higher temperatures may favor earlier germination, water availability will remain the main limiting factor for successful establishment. Integrating physiological parameters into distribution models offers a stronger foundation for seed storage, conservation, and reforestation strategies in the face of changing climatic conditions. Full article

Forest productivity is a critical indicator of forest ecosystem vitality and carbon budget status. Understanding its historical trends and driving mechanisms is essential for assessing forest responses to climate change. Currently, widely used methods for productivity reconstruction, including forest inventories, eddy covariance observations, and remote sensing models, have temporal limitations and cannot adequately meet the demands of long-term ecological research. Tree-ring data, with their advantages of annual resolution and extended time series, have become an important tool for reconstructing historical forest productivity. Research has demonstrated that tree-ring width, stable isotopes, wood density, and anatomical properties are closely related to forest productivity. Mechanistic studies indicate that the climate–canopy–stem coupling relationship exhibits three key nonlinear characteristics: the bidirectional threshold effect of precipitation, the inverted U-shaped temperature response, and the carbon allocation “legacy effect”. Correlation analyses show that the optimal response period between tree rings and productivity is concentrated primarily in the growing season or summer, reflecting the critical regulatory role of temperature and moisture on tree growth. Based on this understanding, existing research has focused predominantly on mid- to high-latitude temperate forests in the Northern Hemisphere that are sensitive to climate, with tree-ring chronologies from arid regions showing stronger correlations with forest productivity. Given current progress and existing limitations, future research should address the impact of stand dynamics on reconstruction accuracy, strengthen linkages between vegetation indices and tree-ring data, integrate belowground productivity, and deepen understanding of the physiological mechanisms underlying forest productivity. Full article

Accurately characterizing historical drought events is critical for understanding their spatial and temporal variability and for improving future drought projections. This study investigates extreme drought years across Europe using three complementary drought indicators: the Palmer drought severity index (PDSI, based on tree-ring width), the standardized precipitation evapotranspiration index (SPEI, based on stable oxygen isotopes in tree rings), and the soil moisture index (SMI, based on high-resolution climate modeling). We analyze the common period 1766–2018 simultaneously across all three reconstructions to enable direct cross-indicator comparisons, a scope not typical of prior single-indicator studies. When analyzing year-to-year variability, the driest European years differ by indicator (PDSI—1874, SPEI—2003, and SMI—1868). Quantitatively, the values exhibited are as follows: PDSI 1874 (M = −1.97; A = 64.4%), SPEI 2003 (M = −1.16; A = 90.1%), and SMI 1868 (M = 0.21; A = 83.4%). Multi-year extremes also diverge: while PDSI identifies 1941–1950 as the driest years (M = −0.82; A = 42.1%), SPEI highlights 2011–2018 (M = −0.36; A = 46.6%), and SMI points to 1781–1790 as the driest years, followed by 2011–2018. Trends in drought-covered areas show a significant European-scale increase for SMI (+0.52%/decade, p < 0.05) and regional increases for MED in SMI (~+1.1%/decade, p < 0.001) and for CEU in SPEI (+0.42%/decade, p < 0.05) and SMI (+0.6%/decade, p < 0.001). At the regional scale (Mediterranean—MED, central Europe—CEU, and northern Europe—NEU), the driest years/decades and spatial footprints vary by indicator, yet all the indicators consistently identify drought hotspots such as the MED. We also found that drought is significantly influenced by large-scale atmospheric drivers. A canonical correlation analysis (CCA) between summer geopotential height at 500 mb (Z500) and drought reconstructions indicates that drought-affected regions are, in general, associated with atmospheric blocking. The canonical series are significantly correlated at r = 0.82 (p < 0.001), with explained variances of 12.78% (PDSI), 8.41% (SPEI), and 14.58% (SMI). Overall, our study underscores the value of multi-indicator approaches: individual indicators provide distinct but complementary perspectives on European drought dynamics, improving the historical context for assessing future risk. Full article

Climate change is driving the need for forest management strategies that simultaneously enhance ecosystem resilience and contribute to climate change mitigation. Voluntary carbon markets (VCMs), regulated in the European Union by the Carbon Removal Certification Framework (CRCF), offer potential financial incentives for such management, but eligibility criteria—particularly biodiversity requirements—limit the applicability of certain species. This study assessed the ecological and economic outcomes of six alternative management scenarios for a 4.7 ha, 99-year-old Scots pine (Pinus sylvestris) stand in western Hungary, comparing them against a business-as-usual (BAU) regeneration baseline. Using field inventory data, species-specific yield tables, and the Forest Industry Carbon Model, we modelled living and dead biomass carbon stocks for 2025–2050 and calculated potential CO₂ credit generation. Economic evaluation employed total discounted contribution margin (TDCM) analyses under varying carbon credit prices (€0–150/tCO₂). Results showed that an extended rotation yielded the highest carbon sequestration (958 tCO₂ above BAU) and TDCM but was deemed operationally unfeasible due to declining stand health. Black locust (Robinia pseudoacacia) regeneration provided high mitigation potential (690 tCO₂) but was ineligible under CRCF rules. Grey poplar (Populus × canescens) regeneration emerged as the most viable option, balancing biodiversity compliance, climate adaptability, and economic return (TDCM = EUR 22,900 at €50/tCO₂). The findings underscore the importance of integrating ecological suitability, market regulations, and economic performance in planning carbon farming projects, and highlight that regulatory biodiversity safeguards can significantly shape feasible mitigation pathways. Full article